Following Roux-en-Y gastric bypass (RYGB), a type of bariatric surgery, many patients exhibit a reduction in taste preference for sweet and fatty foods, although this effect may only be temporary, according to new research from Binghamton University, State University of New York.

Obesity is a growing epidemic worldwide and a leading cause of death alongside heart disease and smoking. Bariatric surgery, specifically RYGB, is the most effective treatment for obesity.

"People who have this surgery are what we call morbidly obese, meaning that they are at least 100 pounds overweight, and in many cases are diabetic," said Patricia DiLorenzo, professor of psychology at Binghamton University. "It's life or death for them."

RYGB makes the stomach much smaller into what is called a pouch. This pouch bypasses part of the small intestine, so when you eat, your food enters a smaller stomach and empties right into the small intestine. This means that people cannot eat large meals anymore, leading to weight loss. However, taste and odor preferences are also known to change after surgery, and their potential involvement with the noted weight loss is a question of study.

DiLorenzo and her research team investigated food and odor preference changes following RYGB and compared them to changes in body-mass index post-surgery. To assess food and odor preferences before and after surgery, patients filled out the Self-Assessment Manikin, which assesses pleasure and arousal responses to an object. Patients were presented with pictures of foods representing the five taste qualities of sweet, sour, salty, bitter and umami, as well as four odors. They were then asked to rate their preferences. BMI data were collected before and after surgery.

"Most people before their surgery, their favorite foods are just what you'd expect -- ice cream, French fries, burgers, pizza," said DiLorenzo. "But afterwards, their favorite food was salad, for example. Twenty percent of people said that their favorite foods were vegetables. Those people -- the ones who said they changed their taste preferences -- lost the most weight."

DiLorenzo and her team also found that people who liked coffee more post-surgery were also the people that lost the most weight. Coffee and vegetables share a bitter flavor, indicating that post RYGB surgery, some patients' taste preferences shifted from high-fat and sweet foods to ones where bitter tastes were less aversive. Patients who experienced this effect lost the most weight and had lower BMIs in the long run after surgery.

However, these altered food preferences generally trend back towards pre-surgery preferences over time. Additionally, the rate of weight loss lessens as time increases post-surgery.

"The lion's share of the weight is lost in the first year," said DiLorenzo. "After that, your weight stabilizes."

Despite the risk for some patients to regain weight post-surgery, the majority of patients successfully lose and keep the weight off.

"People have the view that most people gain the weight back after RYGB surgery, and that's not true," said DiLorenzo. "Eighty percent of the people keep the weight off. In Western medicine, this is the most effective treatment for obesity."


State-of-the-art detectors that screen out online hate speech can be easily duped by humans, shows new study.

Hateful text and comments are an ever-increasing problem in online environments, yet addressing the rampant issue relies on being able to identify toxic content. A new study by the Aalto University Secure Systems research group has discovered weaknesses in many machine learning detectors currently used to recognize and keep hate speech at bay.

Many popular social media and online platforms use hate speech detectors that a team of researchers led by Professor N. Asokan have now shown to be brittle and easy to deceive. Bad grammar and awkward spelling -- intentional or not -- might make toxic social media comments harder for AI detectors to spot.

The team put seven state-of-the-art hate speech detectors to the test. All of them failed.

Modern natural language processing techniques (NLP) can classify text based on individual characters, words or sentences. When faced with textual data that differs from that used in their training, they begin to fumble.

'We inserted typos, changed word boundaries or added neutral words to the original hate speech. Removing spaces between words was the most powerful attack, and a combination of these methods was effective even against Google's comment-ranking system Perspective,' says Tommi Gröndahl, doctoral student at Aalto University.

Google Perspective ranks the 'toxicity' of comments using text analysis methods. In 2017, researchers from the University of Washington showed that Google Perspective can be fooled by introducing simple typos. Gröndahl and his colleagues have now found that Perspective has since become resilient to simple typos yet can still be fooled by other modifications such as removing spaces or adding innocuous words like 'love'.

A sentence like 'I hate you' slipped through the sieve and became non-hateful when modified into 'Ihateyou love'.

The researchers note that in different contexts the same utterance can be regarded either as hateful or merely offensive. Hate speech is subjective and context-specific, which renders text analysis techniques insufficient as stand-alone solutions.

The researchers recommend that more attention be paid to the quality of data sets used to train machine learning models -- rather than refining the model design. The results indicate that character-based detection could be a viable way to improve current applications.